DataFramed

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Aug 2026

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About the show

Welcome to DataFramed, a weekly podcast exploring how artificial intelligence and data are changing the world around us. On this show, we invite data & AI leaders at the forefront of the data revolution to share their insights and experiences into how they lead the charge in this era of AI. Whether you're a beginner looking to gain insights into a career in data & AI, a practitioner needing to stay up-to-date on the latest tools and trends, or a leader looking to transform how your organization uses data & AI, there's something here for everyone. Join host Richie Cotton as he delves into the stories and ideas that are shaping the future of data. Subscribe to the show and tune in to the latest episode on the feed below.

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August 17, 202652 min

#373 What Do Your Colleagues Do All Day? (The Value of Institutional Knowledge & AI for Process Reengineering) | Jennifer Smith, CEO at Scribe

Four years into the AI boom, headlines still promise agents that will run entire departments, yet most companies can't point to the transformation they were sold. The gap isn't intelligence — today's models are remarkably capable — it's context: no model arrives knowing how your company actually gets things done. For anyone tasked with deploying AI at work, this raises pressing questions. What does it take to turn generic intelligence into something that understands your specific operations? And why do so many well-funded AI initiatives stall before they ever reach production? Jennifer Smith is Co-Founder and CEO of Scribe, the Workflow AI platform used by more than 6 million people and 94% of the Fortune 500. Under her leadership, Scribe has surpassed $100M in ARR and raised $75M at a $1.3B valuation. Before founding Scribe, Jennifer spent three years at Greylock Partners interviewing 1,200 C-suite executives about the problems they were trying to solve, and previously worked at Coatue Management and McKinsey & Company. She holds an MBA from Harvard and a BA from Princeton. In the episode, Richie and Jennifer explore why AI agents haven't taken over knowledge work yet, harnessing institutional knowledge as "specialized intelligence," mapping enterprise workflows with LLMs, building the business case and ROI for AI transformation, balancing top-down and bottom-up change management, and the agency-driven skills that matter most in an AI-native workplace, and much more. Links Mentioned in the Show: Scribe (Jennifer's company) McKinsey & Company Aaron Levie , CEO of Box, followed by Jennifer on X Jaya Gupta , Partner at Foundation Capital Connect with Jennifer: LinkedIn AI-Native Course: Intro to AI for Work Related Episode: AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop, EVP at WNS New to DataCamp? Learn on the go using the DataCamp mobile app . Empower your business with world-class data and AI skills with DataCamp for business .

August 10, 202648 min

#372 Bulletproof Large Scale Data Science with Srini Raghavan, Chief Product Officer at Freshworks

Software buying decisions used to be made once, by someone far removed from the people actually using the tool. That model is breaking down. Teams now expect software to work out of the box, without weeks of setup, integration, and configuration before anyone sees value. At the same time, a growing share of "users" aren't people at all — they're AI agents calling the same systems through APIs and chat interfaces. That raises a set of questions worth sitting with: what happens to user experience when the user isn't human? Can personalization and simplicity coexist, or is one always traded for the other? And as building software gets cheaper, what actually separates a good product from a cluttered one? Srini Raghavan is Chief Product Officer at Freshworks, where he leads product strategy for the company's AI-powered customer and employee experience software. He previously served as Chief Product Officer at RingCentral and SVP of Product at Five9, and holds an MBA from the University of Chicago Booth School of Business. In the episode, Richie and Srini explore the SaaS consolidation trend and why the "SaaSpocalypse" prediction missed the point, building software that works for both humans and AI agents, how MCP and modular architecture are reshaping product design, the rise of the "product builder" role replacing specialized titles, customer feedback loops and cohort-based A/B testing, judgment as the most important AI-era career skill, and much more. Links Mentioned in the Show: • Fresh Service — https://www.freshworks.com/freshservice/ • Jaya Gupta's Webinar at RADAR - https://app.datacamp.com/learn/webinars/whats-next-rethinking-analytics-for-the-ai-human-era • Freddy AI Agent Studio — https://www.freshworks.com/freshservice/ai-agent-studio/ • Figma Make — https://www.figma.com/make/ • Cursor — https://cursor.com • NotebookLM — https://notebooklm.google/ • Marc Andreessen's "Mexican standoff" comment — https://officechai.com/ai/programmer-product-manager-and-designer-roles-are-merging-into-a-single-builder-role-marc-andreessen/ • Connect with Srini: https://www.linkedin.com/in/srinivasan28/ • AI Tutor Course: Intro to AI for Work — https://www.datacamp.com/courses/introduction-to-ai-for-work • Related Episode: Vibe Coding and the Rise of the Non-Developer Builder with Matt Palmer, Developer Relations at Replit New to DataCamp? Learn on the go using the DataCamp mobile app . Empower your business with world-class data and AI skills with DataCamp for business .

August 3, 202639 min

#371 The Real Reason Your Product Team Needs a Feedback Loop with Todd Olson, CEO at Pendo

Software teams are shipping faster than ever, but speed hasn't solved the oldest problem in the industry: most software still isn't very good. AI coding tools have lowered the barrier to building something, yet they haven't lowered the barrier to building something worth using. As more people who aren't trained software creators start shipping products, a new question is forming across product, design, and engineering teams: if AI can build almost anything, how do you make sure it builds the right thing, and builds it well? Todd Olson is co-founder and CEO of Pendo, the product experience platform he started in 2013. Before that, he held product and engineering roles at Rally Software, Red Hat, Cisco, and Google. He's the author of The Product-Led Organization and has led Pendo through raising over $356M in venture funding while growing to 2,300+ customers. In the episode, Richie and Todd explore why bad software still gets built, how much context AI coding agents need before they can be trusted, using behavioral data and "rage prompts" to catch what's actually frustrating users, the shift toward headless and agentic software design, how product, design, and engineering roles are splitting apart, managing one-way-door risk during AI transformation, and much more. Links Mentioned in the Show: Jeff Bezos’s one-way door / two-way door decision framework: 2015 Amazon shareholder letter HubSpot’s 2024 terms-of-service backlash Ramp Stripe Fin (Intercom’s AI agent), recently announced to be acquired by Salesforce Anthropic / Claude Code Connect with Todd AI-Native Course: Intro to AI for Work Related Episode: The Data Team’s Agentic Future with Ketan Karkhanis, CEO at ThoughtSpot New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

July 27, 202644 min

#370 Failure is Data (and Other Career Advice) | Todd Dewett, Leadership Author & Speaker

As AI takes over more technical and routine work, the skills that set data and AI professionals apart are shifting. Raw technical ability and a high IQ still matter, but they are becoming table stakes as tools get more capable and teams get smarter. What increasingly separates people is harder to automate: communication, self-awareness, authenticity, and the ability to keep learning through failure. For anyone building a career in this space, that raises real questions. Which skills are actually worth investing in now? What holds up as AI advances? And how do you keep growing once you have already had some success? Dr. Todd Dewett is one of the world's most-watched leadership voices — an authenticity expert, bestselling author, and top LinkedIn Learning instructor whose courses have reached more than 25 million people across 100+ countries. After beginning his career at Andersen Consulting and Ernst & Young, he earned a PhD in organizational behavior at Texas A&M and spent a decade as an award-winning professor before going solo. He is a five-time TEDx speaker and the author of Show Your Ink . In the episode, Richie and Todd explore why fear quietly limits careers, treating failure as data rather than a verdict, the people skills that outlast raw IQ, learnable self-awareness, authenticity at work, using AI without losing your voice, getting better at speaking and writing, building habits, escaping the success trap, and much more. Links Mentioned in the Show: • Todd's LinkedIn newsletter (writing + his "Creswall" comic) — https://www.linkedin.com/in/drdewett/ • Todd Dewett on LinkedIn Learning — https://www.linkedin.com/learning/instructors/todd-dewett • Free LinkedIn Learning access via your public library — https://www.linkedin.com/learning • Gemma Leigh Roberts, chartered psychologist — https://www.linkedin.com/in/gemmaleighroberts/ • Erin Shrimpton, chartered organisational psychologist — https://ie.linkedin.com/in/erinshrimpton • Connect with Todd: https://www.linkedin.com/in/drdewett/ • AI-Native Course: Intro to AI for Work • Related Episode: How to Have a Machine Learning Career in 2026 with Marina Wyss New to DataCamp? Learn on the go using the DataCamp mobile app

July 20, 202649 min

#369 How to Become a Top Business Intelligence Analyst | Helen Wall, Founder at Helen Data Design & Microsoft Influencer

Business intelligence has never been only about building charts and writing queries. Most of the work that makes a report trustworthy happens below the surface — in the data models, documentation, and stakeholder conversations that users never see. For analysts, technical skill is just the starting point; understanding what the business actually needs, and why a number exists, matters just as much. So what really separates a competent analyst from a great one? How do you build something that answers the right question, not just any question? And which skills are worth investing in first? Helen Wall is the founder of Helen Data Design and a Microsoft-recognized business intelligence expert and LinkedIn Learning instructor. A former actuary, she has worked across financial reporting, weather data, and consulting projects, and has maintained a running list of monthly Power BI updates for close to five years. She studied math and economics at the University of Washington, and focuses on where data analytics meets design. In the episode, Richie and Helen explore what separates a great business intelligence analyst from an average one, the iceberg model of analytics work, building and using semantic layers, taking over messy legacy projects, documenting for both humans and AI agents, how Power BI has changed over five years, keeping AI outputs consistent and cost-effective, accountability in the age of agents, and much more. Links Mentioned in the Show: • Connect with Helen • Microsoft AI for Good Lab • Power BI monthly feature updates • SQL Server Analysis Services • Power BI Q&A visual • DAX (Data Analysis Expressions) • AI-Native Course: Intro to AI for Work • Related Episode: The Data Team's Agentic Future with Ketan Karkhanis, CEO at ThoughtSpot New to DataCamp? • Learn on the go using the DataCamp mobile app • Empower your business with world-class data and AI skills with DataCamp for business

July 13, 202650 min

#368 AI Agents Are Now Your Database's Main User | Reynold Xin, Co-Founder at Databricks

For forty years, the rule held that transactional and analytical databases had to be separate systems, connected by fragile pipelines that move data from one to the other. That assumption is now being questioned. As AI agents start generating the majority of database activity, the old architecture is being redesigned around speed, scale, and a single copy of governed data. For anyone who works with data day to day, this raises practical questions. Do you still need separate systems for live and historical data? What happens to the pipelines you maintain? And how does your stack change when agents, not people, write most of the queries? Reynold Xin is co-founder and Chief Architect of Databricks. He is one of the original creators of Apache Spark, where he led the design of GraphX, Project Tungsten, and Structured Streaming, co-designed DataFrames, and served as release manager for Spark 2.0. He holds a PhD in Computer Science from UC Berkeley's AMPLab and a degree in Engineering Science from the University of Toronto. In the episode, Richie and Reynold explore self-service analytics with Genie, the ontology layer that grounds AI in enterprise data, handling hallucinations, governance and permissions for AI agents, merging transactional and analytical databases with Lakebase and LTAP, real-time analytics, controlling cost through autoscaling, the future of Spark and classic machine learning, and much more. Links Mentioned in the Show: • Connect with Reynold: https://www.linkedin.com/in/rxin • Genie (Databricks data agent): https://www.databricks.com/product/genie • Genie Ontology / Genie One: https://www.databricks.com/blog/introducing-genie-one-genie-ontology-and-genie-agents • LTAP (Lake Transactional/Analytical Processing): https://www.databricks.com/company/newsroom/press-releases/databricks-launches-ltap-first-lake-transactionalanalytical • Lakehouse//RT: https://www.databricks.com/blog/introducing-lakehousert-real-time-performance-unified-lakehouse • Lakebase: https://www.databricks.com/product/lakebase • Apache Spark: https://spark.apache.org • AI-Native Course: Intro to AI for Work - https://www.datacamp.com/courses/introduction-to-ai-for-work • Related Episode: AI's Impact on Databases - https://www.datacamp.com/podcast/ais-impact-on-databases New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

July 6, 202651 min

#367 Don't Build on Jell-O: How to Make Agentic AI Reliable with Dan Klein, CTO at Scaled Cognition

Across the AI industry, capability has exploded while trustworthiness has lagged badly behind. The same technology that writes fluent prose can invent a refund policy that was never real, and most of those errors are subtle enough that no one notices. As more teams hand high-stakes work to AI — in banking, healthcare, customer service — the cost of confident mistakes adds up fast. So how common are hallucinations, really? Can chaining models together or adding humans to the loop fix it? And is reliability something you can design into a system from the start? Dan Klein is the CTO and co-founder of Scaled Cognition and a professor of computer science at UC Berkeley, where he leads the Berkeley NLP Group within the Berkeley AI Research (BAIR) Lab. He previously co-founded Semantic Machines, a conversational AI company acquired by Microsoft in 2018. At Scaled Cognition he built APT (Agentic Pretrained Transformer), a frontier model designed from the ground up for reliable, policy-adherent agentic AI. In the episode, Richie and Dan explore why AI reliability has lagged behind capability, how hallucinations hide in plain sight, the limits of humans-in-the-loop and LLM-as-judge, building reliability into model architecture, agentic systems and verifiable actions, test-driven agent development, the skills that stay valuable, digital literacy, and much more. Links Mentioned in the Show: • Connect with Dan: https://www.linkedin.com/in/dan-klein/ • Scaled Cognition: https://www.scaledcognition.com/ • Berkeley NLP Group: https://nlp.cs.berkeley.edu/ • Code smells (Martin Fowler): https://martinfowler.com/bliki/CodeSmell.html • Refactoring, by Martin Fowler: https://martinfowler.com/books/refactoring.html • "Now you have two problems" (Jamie Zawinski quote): https://regex.info/blog/2006-09-15/247 • Lean theorem prover: https://lean-lang.org/ • AI-Native Course: Intro to AI for Work - https://www.datacamp.com/courses/introduction-to-ai-for-work • Related Episode: How to Build AI Your Users Can Trust with David Colwell, VP of AI & ML at Tricentis - https://www.datacamp.com/podcast/how-to-build-ai-your-users-can-trust New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

June 29, 202648 min

#366 Can AI Agents Outperform a Data Scientist? | James Zou, Professor at Stanford University

AI agents are no longer limited to automating routine tasks like customer support or report generation. Research labs and pharmaceutical companies are beginning to deploy teams of specialist AI agents capable of designing experiments, analyzing data, and proposing new hypotheses — in some cases producing results that outperform human experts. For data scientists and researchers, this raises urgent questions: Where do AI agents excel in scientific workflows today, and where do they fall short? How do you build an agent that can genuinely innovate rather than just replicate what's already been done? And what does it take to scale a single model into a fully functioning virtual research team? James Zou is an Associate Professor of Biomedical Data Science, and by courtesy of Computer Science and Electrical Engineering, at Stanford University. He leads the Stanford AI for Science Lab and is affiliated with Together AI. His research focuses on building AI agents for scientific discovery and data science, making AI more reliable and statistically rigorous. He has received a Sloan Fellowship, NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, and faculty awards from Google, Amazon, and Adobe. In the episode, Richie and James explore how AI scientist agents are already outperforming human experts in scientific discovery, the Virtual Lab framework for building teams of specialist AI agents that conduct real research, teaching models to innovate not just imitate through a new training paradigm called "learning to discover," DS Gym for self-improving data science agents, scaling agentic systems from a single model to a Virtual Biotech with tens of thousands of agents, Einstein Arena as the first competition platform built exclusively for AI agents, converting scientific papers into agent-native MCPs through Paper to Agent, and much more. Links Mentioned in the Show: • Virtual Lab (Nature paper) • Einstein Arena • DS Gym • Paper2Agent • Together AI • AlphaFold 2 / Nobel Prize 2024 • Connect with James • AI-Native Course: Intro to AI for Work • Related Episode: #358 How AI Agents Will Work While You Sleep | Ruslan Salakhutdinov New to DataCamp? • Learn on the go using the DataCamp mobile app • Empower your business with world-class data and AI skills with DataCamp for business

June 22, 202653 min

#365 Your 90 Day Blueprint for AI Success with Charlene Li, Author of Winning with AI

Most organizations know AI matters, but few have turned that conviction into a written plan. Ambition and hope are everywhere; a clear roadmap tied to business strategy is rare. For teams on the ground, this gap shows up as scattered initiatives, tools nobody fully uses, and a lot of activity that never adds up to real value. So where do you actually start? How do you move from a long list of use cases to a focused plan you can execute? And who in the organization should own the job of turning AI into business results? Charlene Li is a New York Times bestselling author and strategic advisor who has spent more than two decades helping leaders navigate disruptive change. She founded Altimeter Group, has advised 49 of the Fortune 100, and is the co-author of Winning with AI: The 90-Day Blueprint for Success (with Dr. Katia Walsh). In the episode, Richie and Charlene explore how to get your organization AI-ready in 90 days, why you don't need a separate AI strategy, appointing an AI value owner, creating value beyond efficiency, building AI fluency, Goldilocks governance, why you should kill your AI pilots, and much more. Links Mentioned in the Show: Winning with AI: The 90-Day Blueprint for Success Dr. Katia Walsh (co-author) Moderna Konecta IKEA Andrej Karpathy's LLM Wiki Connect with Charlene: LinkedIn AI-Native Course: Intro to AI for Work Related Episode: Our Data Trends & Predictions for 2026 with Jonathan Cornelissen & Martijn Theuwissen New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business

June 15, 202643 min

#364 How to Enable Agentic Commerce with Nell Thomas, VP of Data at Shopify

AI agents are starting to handle parts of the shopping journey that used to require human judgment — discovery, comparison, checkout. But behind every agent recommendation is a massive, invisible layer of data infrastructure. Product catalogs need to be structured, inventory synced in real time, pricing accurate, and quality signals clear. For data engineers and teams building at companies like Shopify, this shift means rethinking how data flows through systems and what "good enough" quality actually means. How do you ensure data is ready for AI? And how is this reshaping what data teams actually do? Nell Thomas is the VP of Data at Shopify, where she leads a team of approximately 400–500 people across data infrastructure, ML platforms, data engineering, and data science. Her career spans multiple industries including social media (Facebook), e-commerce (Etsy), politics (Hillary for America, Democratic National Committee), and now commerce. She holds an A.B. in Psychology from Harvard University and an M.A. in History & Sociology of Science from the University of Pennsylvania. In the episode, Richie and Nell explore agentic commerce and how AI agents are transforming shopping, the role of data in enabling AI-driven commerce, Shopify's Catalog and Universal Commerce Protocol, data quality requirements for agentic systems, how the data team function is evolving at Shopify, changing skill requirements for data professionals, and Nell's unconventional career path from politics to tech. Links Mentioned in the Show: - Agentic Commerce on Shopify - Universal Commerce Protocol (UCP) vs Agentic Commerce Protocol (ACP) - Shopify Catalog Documentation - Agentic Storefronts — Shopify Sales Channel - ChatGPT — OpenAI's Conversational AI - How Shopify Built Data Infrastructure at Scale Related Scaling Data Quality in the Age of Generative AI New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business